A scientific definition of brain-computer interfaces (BCIs): Essential components, fundamental characteristics, capability boundaries, and scope delimitation_Journal of Biomedical Engineering

Authors：

 FU Yunfa ^1,2 , CHENG Tianjiao ^1,2 , LUO Rongzhang ^1,2 , ZHAO Lei ^2,3 , LI Tianwen ^2,3 , SU Lei ^1,2 ,  XU Jiaping ¹

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technilogy, Kunming 650500, P. R. China;
2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China;
3. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P. R. China;

Corresponding?author：

FU Yunfa, Email: fyf@ynu.edu.cn; XU Jiaping, Email: 2395000438@qq.com

Keywords：

Brain–computer interface; Scientific definition; Scope delineation; Capability boundaries; Neurotechnology

DOI：

10.7507/1001-5515.202511002

Video：

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Abstract Full text Figures/Tables Video References Cited by

Brain–computer interfaces (BCIs) are communication and control systems centered on neural signals that incorporate both the user and the brain into a closed-loop interaction framework, and are widely regarded as a transformative paradigm in human–computer interaction. However, despite the existence of broadly accepted definitions within the research community, the rapid acceleration of BCI translation and commercialization has led to increasing ambiguity in scientific definitions, expansion of conceptual scope, and overstatement of technical capabilities. To address these issues, this paper proposed a scientifically grounded definition of BCIs and systematically analyzed their essential system components and fundamental characteristics. On this basis, the major and specific factors that constrain the capability boundaries of current and foreseeable BCI systems were examined. Furthermore, the scope of BCI was explicitly delineated by distinguishing BCIs from adjacent neurotechnologies based on their functional roles and system characteristics. This work aims to promote a more rigorous and coherent understanding of BCI definitions, scope, and capability limits within the academic community, and to provide essential theoretical foundations for responsible translation and long-term development. By clarifying conceptual boundaries and realistic expectations, it seeks to mitigate risks associated with conceptual generalization and distorted projections in both research and industrial practice, thereby fostering a more rational, robust, and sustainable ecosystem for the BCI field.

Citation： FU Yunfa, CHENG Tianjiao, LUO Rongzhang, ZHAO Lei, LI Tianwen, SU Lei, XU Jiaping. A scientific definition of brain-computer interfaces (BCIs): Essential components, fundamental characteristics, capability boundaries, and scope delimitation. Journal of Biomedical Engineering, 2026, 43(1): 1-7. doi: 10.7507/1001-5515.202511002 Copy

1.	Gao X, Wang Y, Chen X, et al. Brain–computer interface—a brain-in-the-loop communication system. Proc IEEE, 2025, 113(5): 478-511.
2.	Chen Y, Wang F, Li T, et al. Several inaccurate or erroneous conceptions and misleading propaganda about brain–computer interfaces. Front Hum Neurosci, 2024, 18: 1391550.
3.	Wolpaw J R, Birbaumer N, McFarland D J, 等. 腦–機接口——革命性的人機交互. 伏云發, 郭衍龍, 張夏冰, 等譯. 北京: 國防工業出版社, 2020.
4.	Wolpaw J R, Birbaumer N, McFarland D J, 等. 腦–接口原理與實踐. 伏云發, 楊秋紅, 徐保磊, 等譯. 北京: 國防工業出版社, 2017.
5.	Tan D, Nijholt A. Brain-computer interfaces and human-computer interaction// Brain-computer interfaces: Applying our minds to human-computer interaction. London: Springer London, 2010: 3-19.
6.	Li M, He D, Li C, et al. Brain–computer interface speller based on steady-state visual evoked potential: a review focusing on the stimulus paradigm and performance. Brain Sci, 2021, 11(4): 450.
7.	Padfield N, Zabalza J, Zhao H, et al. EEG-based brain–computer interfaces using motor imagery: techniques and challenges. Sensors, 2019, 19(6): 1423.
8.	Fazel-Rezai R, Allison B Z, Guger C, et al. P300 brain–computer interface: current challenges and emerging trends. Front Neuroeng, 2012, 5: 14.
9.	Thomas T M, Singh A, Bullock L P, et al. Decoding articulatory and phonetic components of naturalistic continuous speech from the distributed language network. J Neural Eng, 2023, 20(4): 046030.
10.	Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain–spine interface. Nature, 2023, 618: 126-133.
11.	Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593: 249-254.
12.	Erat K, Sahin E B, Dogan F, et al. Emotion recognition with EEG-based brain–computer interfaces: a systematic literature review. Multimed Tools Appl, 2024, 83(33): 79647-79694.
13.	Craik A, He Y, Contreras-Vidal J L. Deep learning for electroencephalogram classification tasks: a review. J Neural Eng, 2019, 16(3): 031001.
14.	Milyani A H, Attar E T. Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG–fMRI data. Front Hum Neurosci, 2025, 19: 1668935.
15.	Wolpaw J R, Wolpaw E W. Brain–computer interfaces: something new under the sun// Brain–Computer Interfaces: Principles and Practice. Oxford: Oxford Univ Press, 2012: 3-12.
16.	Wolpaw J R, Millán J R, Ramsey N F. Brain–computer interfaces: definitions and principles// Handbook of Clinical Neurology. Amsterdam: Elsevier, 2020, 168: 15-23.
17.	Wolpaw J R, Birbaumer N, McFarland D J, 等. 腦–計算機接口. 伏云發, 王帆, 丁鵬, 等譯. 北京: 國防工業出版社, 2023.
18.	Beck S, Liberman Y, Dubljevi? V. Media representation of the ethical issues pertaining to brain–computer interface (BCI) technology. Brain Sci, 2024, 14(12): 1255.
19.	Fu Y, Chen X, Hu Y. Correct understanding of brain–computer interfaces. J Neurorestoratol, 2024, 12: 100139.
20.	Fu Y, Xue Y, Chen X, et al. Brain–computer interface (BCI) in clinical neurorestorative practices. J Neurorestoratol, 2025, 13(2): 100188.
21.	Xue Y, Chen Y, Wang F, et al. Applications and interrelationships of brain function detection, brain–computer interfaces, and brain stimulation: a comprehensive review. Cogn Neurodyn, 2025, 19: 161.
22.	伏云發, 魯海晨. 腦機接口(BCI)的技術成熟度與泡沫風險分析: 從研究到產業轉化的考量. 生物醫學工程學雜志, 2025, 42(4): 651-659.
23.	Chen Y, Wang F, Li T, et al. Considerations and discussions on the clear definition and definite scope of brain–computer interfaces. Front Neurosci, 2024, 18: 1449208.
24.	Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
25.	Tai P, Ding P, Wang F, et al. Brain–computer interface paradigms and neural coding. Front Neurosci, 2024, 17: 1345961.
26.	呂曉彤, 丁鵬, 李思語, 等. 腦機接口人因工程及應用: 以人為中心的腦機接口設計和評價方法. 生物醫學工程學雜志, 2021, 38(2): 210-223.
27.	Lyu X, Ding P, Li S, et al. Human factors engineering of BCI: evaluation of satisfaction in motor imagery-based BCI. Cogn Neurodyn, 2023, 17(1): 105-118.
28.	Wolpaw J R, Wolpaw E W. BCI signal processing: feature translation// Brain–Computer Interfaces: Principles and Practice. Oxford: Oxford Univ Press, 2012: 3-12.
29.	Pan H, Ding P, Wang F, et al. Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and online BCI usage. Front Hum Neurosci, 2024, 18: 1429130.
30.	Sitaram R, Ros T, Stoeckel L, 等. 神經反饋原理與實踐. 伏云發, 龔安民, 南文雅, 譯. 北京: 電子工業出版社, 2021.
31.	Preetha S, Sasikala M. Design of asynchronous low-complexity SSVEP-based brain control interface speller. Comput Biol Med, 2025, 190: 110062.
32.	An Y, Wong J, Ling S H. Development of real-time brain–computer interface control system for robot. Appl Soft Comput, 2024, 159: 111648.
33.	張喆, 陳衍肖, 趙旭, 等. 植入式腦機接口醫學應用倫理規范考量. 生物醫學工程學雜志, 2024, 41(1): 177-183.
34.	Levy N. Neuroethics: Challenges for the 21st century. Cambridge: Cambridge Univ Press, 2007.
35.	Han F, Chen H. Does brain–computer interface-based mind reading threaten mental privacy? Ethical reflections from interviews with Chinese experts. BMC Med Ethics, 2025, 26: 134.
36.	中國人工智能產業發展聯盟. 腦機接口技術在醫療健康領域應用白皮書. 北京: 中國人工智能產業發展聯盟, 2021.

1. Gao X, Wang Y, Chen X, et al. Brain–computer interface—a brain-in-the-loop communication system. Proc IEEE, 2025, 113(5): 478-511.
2. Chen Y, Wang F, Li T, et al. Several inaccurate or erroneous conceptions and misleading propaganda about brain–computer interfaces. Front Hum Neurosci, 2024, 18: 1391550.
3. Wolpaw J R, Birbaumer N, McFarland D J, 等. 腦–機接口——革命性的人機交互. 伏云發, 郭衍龍, 張夏冰, 等譯. 北京: 國防工業出版社, 2020.
4. Wolpaw J R, Birbaumer N, McFarland D J, 等. 腦–接口原理與實踐. 伏云發, 楊秋紅, 徐保磊, 等譯. 北京: 國防工業出版社, 2017.
5. Tan D, Nijholt A. Brain-computer interfaces and human-computer interaction// Brain-computer interfaces: Applying our minds to human-computer interaction. London: Springer London, 2010: 3-19.
6. Li M, He D, Li C, et al. Brain–computer interface speller based on steady-state visual evoked potential: a review focusing on the stimulus paradigm and performance. Brain Sci, 2021, 11(4): 450.
7. Padfield N, Zabalza J, Zhao H, et al. EEG-based brain–computer interfaces using motor imagery: techniques and challenges. Sensors, 2019, 19(6): 1423.
8. Fazel-Rezai R, Allison B Z, Guger C, et al. P300 brain–computer interface: current challenges and emerging trends. Front Neuroeng, 2012, 5: 14.
9. Thomas T M, Singh A, Bullock L P, et al. Decoding articulatory and phonetic components of naturalistic continuous speech from the distributed language network. J Neural Eng, 2023, 20(4): 046030.
10. Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain–spine interface. Nature, 2023, 618: 126-133.
11. Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593: 249-254.
12. Erat K, Sahin E B, Dogan F, et al. Emotion recognition with EEG-based brain–computer interfaces: a systematic literature review. Multimed Tools Appl, 2024, 83(33): 79647-79694.
13. Craik A, He Y, Contreras-Vidal J L. Deep learning for electroencephalogram classification tasks: a review. J Neural Eng, 2019, 16(3): 031001.
14. Milyani A H, Attar E T. Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG–fMRI data. Front Hum Neurosci, 2025, 19: 1668935.
15. Wolpaw J R, Wolpaw E W. Brain–computer interfaces: something new under the sun// Brain–Computer Interfaces: Principles and Practice. Oxford: Oxford Univ Press, 2012: 3-12.
16. Wolpaw J R, Millán J R, Ramsey N F. Brain–computer interfaces: definitions and principles// Handbook of Clinical Neurology. Amsterdam: Elsevier, 2020, 168: 15-23.
17. Wolpaw J R, Birbaumer N, McFarland D J, 等. 腦–計算機接口. 伏云發, 王帆, 丁鵬, 等譯. 北京: 國防工業出版社, 2023.
18. Beck S, Liberman Y, Dubljevi? V. Media representation of the ethical issues pertaining to brain–computer interface (BCI) technology. Brain Sci, 2024, 14(12): 1255.
19. Fu Y, Chen X, Hu Y. Correct understanding of brain–computer interfaces. J Neurorestoratol, 2024, 12: 100139.
20. Fu Y, Xue Y, Chen X, et al. Brain–computer interface (BCI) in clinical neurorestorative practices. J Neurorestoratol, 2025, 13(2): 100188.
21. Xue Y, Chen Y, Wang F, et al. Applications and interrelationships of brain function detection, brain–computer interfaces, and brain stimulation: a comprehensive review. Cogn Neurodyn, 2025, 19: 161.
22. 伏云發, 魯海晨. 腦機接口(BCI)的技術成熟度與泡沫風險分析: 從研究到產業轉化的考量. 生物醫學工程學雜志, 2025, 42(4): 651-659.
23. Chen Y, Wang F, Li T, et al. Considerations and discussions on the clear definition and definite scope of brain–computer interfaces. Front Neurosci, 2024, 18: 1449208.
24. Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
25. Tai P, Ding P, Wang F, et al. Brain–computer interface paradigms and neural coding. Front Neurosci, 2024, 17: 1345961.
26. 呂曉彤, 丁鵬, 李思語, 等. 腦機接口人因工程及應用: 以人為中心的腦機接口設計和評價方法. 生物醫學工程學雜志, 2021, 38(2): 210-223.
27. Lyu X, Ding P, Li S, et al. Human factors engineering of BCI: evaluation of satisfaction in motor imagery-based BCI. Cogn Neurodyn, 2023, 17(1): 105-118.
28. Wolpaw J R, Wolpaw E W. BCI signal processing: feature translation// Brain–Computer Interfaces: Principles and Practice. Oxford: Oxford Univ Press, 2012: 3-12.
29. Pan H, Ding P, Wang F, et al. Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and online BCI usage. Front Hum Neurosci, 2024, 18: 1429130.
30. Sitaram R, Ros T, Stoeckel L, 等. 神經反饋原理與實踐. 伏云發, 龔安民, 南文雅, 譯. 北京: 電子工業出版社, 2021.
31. Preetha S, Sasikala M. Design of asynchronous low-complexity SSVEP-based brain control interface speller. Comput Biol Med, 2025, 190: 110062.
32. An Y, Wong J, Ling S H. Development of real-time brain–computer interface control system for robot. Appl Soft Comput, 2024, 159: 111648.
33. 張喆, 陳衍肖, 趙旭, 等. 植入式腦機接口醫學應用倫理規范考量. 生物醫學工程學雜志, 2024, 41(1): 177-183.
34. Levy N. Neuroethics: Challenges for the 21st century. Cambridge: Cambridge Univ Press, 2007.
35. Han F, Chen H. Does brain–computer interface-based mind reading threaten mental privacy? Ethical reflections from interviews with Chinese experts. BMC Med Ethics, 2025, 26: 134.
36. 中國人工智能產業發展聯盟. 腦機接口技術在醫療健康領域應用白皮書. 北京: 中國人工智能產業發展聯盟, 2021.

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